Among the uncertainties, the elastic response pseudo-acceleration corresponding to the isolated period is assumed as the relevant random variable and, by means of the Latin Hypercube Sampling technique, the input data have been defined in order to develop 3D inelastic time-history analyses.
Four different criteria (C1-C4) for measuring the validity of repeated exposure data have been defined as: C1 Scenario tuning: Can the repeated scenario be tuned so that the steering manoeuvre is initiated under similar conditions in both test settings (unexpected and repeated)?
For the import of new mutein data, a simple, standardized, spreadsheet-based data format has been defined.
Data reuse has been defined as a related concept of big data because we think that there might be some confusion between these two terms: data reuse is the fact of using for decisional purposes data that were collected routinely for transactional purposes, whereas big data is related to the size of the data collection.
Assuming appropriate density probability functions for each random variable and adopting the Latin Hypercube Sampling (LHS) method for random sampling, the input data set has been defined.
This is because the unit and unit type are common concepts for scientific data so it has been defined in the CML Schema.
For that purpose, the concept of service tracking has been added to Edition 2. For that purpose, a data object instance has been defined for each kind of service, which mirrors the values of the service parameters.
Database has been built and the outline of the data storage structure has been defined.
Data distribution service (DDS) has been defined by the OMG to provide a standard data-centric publish-subscribe programming model and specification for distributed systems.
Exchange of Digital Imaging and Communications in Medicine (DICOM) data using such media has been defined in the DICOM standard, including the requirements of the storing formats, structure and profiles [1, 2, 3].
